Enterprise AI Analysis
A Temporally Dynamic Feature-Extraction Framework for Phishing Detection with LIME and SHAP Explanations
This research introduces a dynamic Explainable AI (XAI) approach for phishing detection that integrates temporally aware feature extraction with dual interpretability through LIME and SHAP. It captures behavioural and temporal patterns using a sliding window aggregation method, demonstrating strong performance with XGBoost achieving 94% accuracy and Random Forest 93%. The framework enhances trustworthiness and practicality without excessive computational overhead.
Executive Impact Summary
Our analysis reveals how integrating dynamic temporal features and explainable AI can significantly improve phishing detection accuracy and transparency, offering tangible benefits for enterprise security.
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Phishing Detection Accuracy (XGBoost)
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Reduced False Negatives Annually
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Improvement in Interpretability
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XGBoost Training Time (Fast Deployment)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Temporally Dynamic Feature Extraction
This research introduces a novel framework for phishing detection that integrates temporally aware feature extraction with Explainable AI (XAI). By simulating email reading progression and aggregating features via a sliding window, the system captures dynamic behavioral and temporal patterns. This allows models to adapt to evolving phishing tactics, a significant improvement over static detection methods.
Enterprise Process Flow
Timestamp Extraction
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Data Preprocessing
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Session Log Creation
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Sliding Window Construction
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Aggregate Window Features
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Model Training & XAI
High-Performance Dynamic Detection
The framework achieved strong detection performance, with XGBoost reaching 94% accuracy and Random Forest 93% accuracy. These ensemble models outperformed Multi-Layer Perceptron (MLP) and Logistic Regression, showcasing their robustness in handling dynamic features and reducing false negatives. The use of aggregated, window-level features enhances the model's ability to detect sophisticated and evolving phishing attempts that static models often miss.
94%
Peak Phishing Detection Accuracy (XGBoost)
Transparent Explainable AI (XAI)
A key contribution is the integration of LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations) for dual interpretability. LIME provides instance-level explanations, showing which features most strongly support a given prediction. SHAP offers global insights into feature importance, revealing temporal cues (e.g., base_hour, base_year) and money-related terms as dominant drivers for classification decisions. This transparency fosters user trust and enables actionable security insights.
Case Study: Identifying Key Phishing Indicators with SHAP
Challenge: Traditional black-box models lack clarity on why an email is flagged as phishing, hindering trust and rapid response.
Solution: SHAP analysis applied to our dynamic feature set revealed that 'base_hour' and 'base_year' are highly influential temporal features. Phishing attempts often show distinct temporal patterns, with attackers leveraging specific times of day or periods (e.g., older email timestamps for social engineering) to maximize impact.
Impact: Enterprise security teams can now focus on emails with suspicious temporal metadata, rapidly understanding the context and intent behind potential threats. This shifts from reactive analysis to proactive, context-aware threat hunting, reducing detection time and increasing accuracy.
Benchmarking Against Static Approaches
Our dynamic framework, combining engineered temporal features with efficient classical models like Random Forest and XGBoost, achieves competitive performance (93-94% accuracy) while remaining lightweight, scalable, and highly interpretable. This contrasts with more complex deep-learning architectures that, despite high accuracy, often lack transparency and incur higher computational costs, making them less practical for real-time enterprise deployment.
| Feature | Our Dynamic XAI Approach | Static ML/Deep Learning (Typical) |
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| Feature Extraction |
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| Explainability |
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Calculate Your Potential ROI with Dynamic Phishing Detection
Estimate the financial and operational benefits of implementing an advanced, explainable phishing detection system tailored to your enterprise.
Estimated Annual Savings
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Annual Hours Reclaimed
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Your AI Implementation Roadmap
Our structured approach ensures seamless integration and maximum impact for your enterprise.
Phase 1: Discovery & Strategy
Collaborate to understand your current security posture, data landscape, and specific phishing threats. Define clear objectives and a tailored strategy for dynamic feature extraction and XAI integration.
Phase 2: Data Engineering & Feature Development
Implement the temporal feature extraction pipeline, including timestamp parsing, session log creation, and sliding window aggregation. Prepare and validate your enterprise email dataset.
Phase 3: Model Training & XAI Integration
Train and fine-tune machine learning models (XGBoost, Random Forest) on dynamic features. Integrate LIME and SHAP for comprehensive local and global interpretability of model decisions.
Phase 4: Deployment & Monitoring
Deploy the explainable phishing detection system within your existing infrastructure. Establish continuous monitoring for performance, feature drift, and ongoing XAI-driven insights to adapt to new threats.
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